Emotion classification using flexible analytic wavelet transform for electroencephalogram signals

被引：34

作者：

Bajaj V. ^{[1
]}

Taran S. ^{[1
]}

Sengur A. ^{[2
]}

机构：

[1] PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur

[2] Electrical and Electronics Engineering Department, Technology Faculty, Firat University, Elazig

来源：

Health Information Science and Systems | / 6卷 / 1期

关键词：

Electroencephalogram; Emotion classification; Flexible analytic wavelet transform; k-nearest-neighbor;

D O I：

10.1007/s13755-018-0048-y

中图分类号：

学科分类号：

摘要：

Emotion based brain computer system finds applications for impaired people to communicate with surroundings. In this paper, electroencephalogram (EEG) database of four emotions (happy, fear, sad, and relax) is recorded and flexible analytic wavelet transform (FAWT) is proposed for the emotion classification. FAWT analyzes the EEG signal into sub-bands and statistical measures are computed from the sub-bands for extraction of emotion specific information. The emotion classification performance of sub-band wise extracted features is examined over the variants of k-nearest-neighbor (KNN) classifier. The weighted-KNN provides the best emotion classification performance 86.1% as compared to other KNN variants. The proposed method shows better emotion classification performance as compared to other existing four emotions classification methods. © 2018, Springer Nature Switzerland AG.

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